Implement a Data Science and Machine Learning Solution for AI with Microsoft Fabric (DP-604)
Course Outline
Dive into the data science process and discover how to train machine learning models for artificial intelligence within Microsoft Fabric. This course covers loading data into a Lakehouse, exploring data using notebooks, preprocessing data with Data Wrangler, training models with MLflow, and generating batch predictions with deployed models.
Implement a Data Science and Machine Learning Solution for AI with Microsoft Fabric (DP-604) Benefits
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In this course, you will learn how to:
- Load and manage data in a Lakehouse within Microsoft Fabric.
- Utilize notebooks for comprehensive data exploration.
- Preprocess data using Microsoft Fabric's Data Wrangler for optimized model training.
- Train and manage machine learning models with MLflow, tracking experiments effectively.
- Generate batch predictions to apply AI in practical scenarios.
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Prerequisites
- Familiarity with basic data concepts and terminology.
- Understanding of the Python programming language and machine learning frameworks like scikit-learn is advantageous.
DP-604 Course Outline
Introduction to Data Science in Microsoft Fabric
Get acquainted with the data science process within Microsoft Fabric, including managing data, notebooks, experiments, and models.
Explore Data for Data Science with Notebooks
Utilize Microsoft Fabric notebooks for in-depth data exploration, covering loading data, understanding data distribution, handling missing data, and applying advanced exploration techniques.
Preprocess Data with Data Wrangler in Microsoft Fabric
Learn how to preprocess data effectively using Data Wrangler in Microsoft Fabric, including data cleaning, handling missing values, and feature transformation.
Train and Track Machine Learning Models with MLflow
Discover how to train machine learning models in notebooks, track experiments with MLflow, and manage models efficiently within Microsoft Fabric.
Generate Batch Predictions using Deployed Models
Learn to utilize machine learning models deployed in Microsoft Fabric to generate batch predictions, enhancing data enrichment and analysis capabilities.
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